Diabetes Prediction Using Machine Learning Classification Algorithms

Chronic diabetes is one of the most common diseases worldwide and making the diagnosis process simpler and quicker would have a huge effect on the treatment process.
The fundamental goal of this work is to prepare and carry out diabetes prediction using various machine learning techniques and Conduct
output analysis of those techniques to find the best classifier with the highest accuracy. This study examines diabetes prediction by
taking different diabetes disease-related attributes. We use the Pima Indian Diabetes Dataset and applied the Machine Learning
classification methods like K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM) for diabetes prediction. The models used in this analysis have various degrees of accuracy.
This study shows a model that can correctly forecast diabetes. In comparison to other machine learning methods, the random forest has
high accuracy in forecasting diabetes, according to the findings of this study.